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Social Media Data Model

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Post  kclark Tue Mar 04, 2014 10:51 pm

Hi All,

I'm looking to design a dimensional model for managing social media...i.e. twitter, blogs, etc. Has anyone ever worked with this? What would be the facts and dimensions? I have so many designs going on in my head, I'm looking for some direction :-).

Thanks in advance,
Krystal

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Post  TheNJDevil Wed Mar 05, 2014 10:07 am

I would start by going back to the business and getting requirements on what they are wanting to get out of this social media data. What are the ultimate goals or what problems are they trying to solve that would possibly be contained in this data? Once you know that, you can model facts and dimension more easily.

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Post  kclark Wed Mar 05, 2014 10:23 am

Thanks NJ,

Unfortunately, this is new so the business is looking for the basics. What is a common model that is used? That way I can talk my through this...I'm looking to get an idea of what a model others have used look like. Offhand, I can say there is an interest in looking at what tools/social media are utilized more and looking to monitor customer responses on Products. Does that help any?

Thanks,
Krystal

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Post  ngalemmo Wed Mar 05, 2014 1:40 pm

It boils down to the basics: who, what, where, when, and why.  The challenge is processing the data so you can answer those questions.

When is easy, and where may not be a concern.  Who starts getting messy, particularly if you are trying to relate something like twitter handle with a customer.  This requires creative strategies to try to get such people to identify themselves to you, such as getting them to link their twitter, Facebook, and email (and whatever else) accounts to their loyalty account.  At the start, just capturing the handle will give you a sense of frequency and general overall perceptions from the population.  You can get more sophisticated later.

What and why are the most difficult due to the ambiguity of the source.  There are a variety of natural language processing strategies to try to get meaning from such data.  Such processes attach attributes to the message ranking it to various degrees of positive and negative, products mentioned, and so forth. HADOOP or similar technologies are often used for this process due to the volumes involved.

Finally, you can go as far as capturing all text and build a keyword structure around it.  However, the value of such data degrades very quickly over time as well as grows at a significant rate.  Most usually just store the attributes from the NL process, and maybe keep the raw data for a short period of time.
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Post  kclark Wed Mar 05, 2014 2:57 pm

Wow! That's a lot to consider. So hmmm, let's see. Yes, that was my first thought, associating users to actual customers. That sounds like it would take a whole lot of participation from the users. Putting that aside, my thoughts are more along the lines of summaries. So 1) Summary of usage of source (FaceBook - 21K, Twitter -100K), 2) Summary of ratings by Product (Tylonel 4; Aspirin 2), and 3) Summary of product preference by company

Totally agree on the volume

What about that?

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Post  ngalemmo Wed Mar 05, 2014 3:57 pm

Its a start.
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Post  kclark Wed Mar 05, 2014 5:41 pm

Hahaha! Gee thanks. :-) Specifically speaking, I have set designed this:

Dimensions:

Users
Source
Product
Date

Facts:
Source Utilization
Blogs
Communities (i.e. Facebook)
Videos (i.e. Youtube)
Movements (i.e. Twitter)


Is this too granular? Or should I have more of the summary tables I mentioned above?


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Post  ngalemmo Wed Mar 05, 2014 6:54 pm

I'm not trying to blow you off, but honestly its a pretty broad area and its really hard to comment without spending time looking at what you are collecting from where. As far as summary versus detail goes, I always prefer detail as it gives you opportunities that may be lost in summaries. One option is to collect detail for a short term (frequent historical purges) and maintain summaries for the long term. But, if your not keeping the raw text, the detailed attributes of the text don't take a lot of space.
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Post  nick_white Thu Mar 06, 2014 4:23 am

Hi - at the risk of moving this discussion backwards, I would return to TheNJDevil's comment about getting the business to articulate their requirements. One of the major reasons why so many IT projects fail (to a greater or lesser extent, and not just DW projects) is because they are IT-driven and not business-driven.
If the business cannot articulate their reporting requirements then you should put the project on hold until they can - why is the business spending time and money on building a reporting system if they have no reporting requirements?
You can obviously help/guide them towards being able to articulate their requirements - and if what you are proposing here is a quick and dirty prototype to help the process then that's fine; but in that case I wouldn't worry about granularity too much at this time.

Regards,

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Post  kclark Thu Mar 06, 2014 9:51 am

Thanks,

Nick - Yes it is a quick and dirty prototype just to get the business to visualize the possibilities.  Then we will get into the requirements.  

NG - Thanks for some more feedback.  Going back to your comment regarding Hadoop/data processing.  Hadoop/Cassandra/etc, are typically used to perform ETL and storage of Big data?  Something like DataStage or Informatica wouldn't be able to process the enormous volume of data, correct?

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Post  TheNJDevil Thu Mar 06, 2014 10:28 am

Krystal, Seek out whitepapers to give you more clear examples of what has been done and what kind of effort it took to get those projects done. You are in the same boat I was 2 years ago. Build it and they will then see what can be done and that will spark better ideas to build off of. The problem with that is that you do it at little to no cost, and if it is successful, management then has the expectation of being able to deliver these successes with little to no cost.

Check out whitepapers that cover similar topics to what you are trying to do (social media analysis shouldn't be difficult to find). Those would be better than a quick and dirty prototype of unknown purpose or outcome. To find success, you have to first define what will make this a successful project, then work toward that. "Build it and they will come" is just a path to frustration in the long run.

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Post  kclark Thu Mar 06, 2014 10:42 am

NJ,

Totally AGREE! Behind closed doors we say the SAME thing. ARGH! This initiative is very conceptual and the objective is just generic entity models and recommendations. I'm not even doing a prototype or POC; just because the effort and time allotted don't align. I'm just looking for a quick word of direction. But if the advice is whitepapers...I may end up having to shadow my way through this part, since I'm really short on time to read. :-(

Thanks again for your help!

Krystal

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Post  ngalemmo Thu Mar 06, 2014 12:17 pm

One of the ways you can use HADOOP is as a transformation engine to prep data for loading into a DW. INFA and Datastage can do the same thing. The decision to use HADOOP for such a task is driven by data volume above everything else. If you have reasonable volumes that can be handled with you existing toolset, by all means, use them.
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